Lasting Impacts From Stop & Frisk

Evidence From The Housing Market in New York City

Alex Cardazzi

Old Dominion University

Background & Timeline

Background & Timeline

via The Wall Street Journal

Background & Timeline

  • Starting in 1994, “Stop, Question & Frisk” is a strategy resulting from “Broken Window Policing”.
  • On January 31st, 2008, a federal class action lawsuit claimed Stop & Frisk “encourages racial profiling and unconstitutional detainment”.
  • On May 16th, 2012, a judge granted class certification for the lawsuit.
  • The trial begins on March 18th, 2013 and ends on August 12th, 2013 with a judge ruling Stop & Frisk to be unconstitutional.
  • Stop & Frisk still occurs in New York City today.

Background & Timeline

Link to Google Trends

Background & Timeline

Stop & Frisk by Day in New York City

Background & Timeline

Crime Reports by Day in New York City

Literature Review

Crime (and risk of crime) reduces housing prices

Tita, Petras, and Greenbaum (2006); Ihlanfeldt and Mayock (2010); Linden and Rockoff (2008); Pope (2008); Caudill, Affuso, and Yang (2015); Kim and Lee (2018)

Stop & Frisk marginally reduces crime
MacDonald, Fagan, and Geller (2016); Bacher-Hicks and Campa (2021b)

Literature Review

Other impacts of Stop & Frisk:
Decreased educational outcomes (Legewie and Fagan 2019; Bacher-Hicks and Campa 2021a)
Decreased views of police legitimacy (Tyler, Fagan, and Geller 2014)
Decreased mental health (Geller et al. 2014)
Housing Prices:
(Friedman 2015) shows that properties exposed to more intense Stop & Frisk behavior sell for lower prices
Each additional stop decreases prices by $35 - $250
Using data from 2006 - 2012 (includes recession & avoids court case)
Does not control for crime

Research Question

Does the way we are policed effect housing prices?

  1. Prices were depressed in the most heavily patrolled areas, and thus increased after the court’s decision.
  2. Homes owned by individuals most likely to be impacted by Stop & Frisk did not increase in price when sold.
    • The increase is driven by white sellers.
    • Benı́tez-Silva et al. (2015) demonstrate that people who buy during hard economic times relatively underestimate the value of their homes.

Data & Methodology

Operation Impact

MacDonald, Fagan, and Geller (2016)

In January 2003, the NYPD deployed roughly two-thirds of its police academy graduates—about 1,500 new police officers—to Impact Zones.

Police Commanders nominated crime “hot spots” within their precincts

Every 6 months, these zones would be re-evaluated and adjusted.

Operation Impact

Operation Impact

Data

  1. New York City (Residential) Property Sales
    • NYC Dept. of Finance
    • Automated City Register Information System (ACRIS)
  2. New York Police Department (NYPD)
    • NYPD Operation Impact Zones\(^{*}\)
    • NYPD Stop & Frisk and Crime Data
  3. American Community Survey Data for New York City
  4. Census Bureau 2000 and 2010 Decennial Census Surnames

Data

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Idea 2: Calculate Difference in Prices between Treatment and Control

This won’t work because there might be pre-existing differences between treatment and control. How can I be sure I am not capturing those effects?

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Idea 2: Calculate Difference in Prices between Treatment and Control

This won’t work because there might be pre-existing differences between treatment and control. How can I be sure I am not capturing those effects?

Idea 3: Combine Ideas 1 and 2

Calculate difference (before and after) for both treatment and control. Whatever happened in the control should have also happened in the treatment. Subtracting the change in the control from the change in the treatment will isolate the treatment effect.

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Idea 2: Calculate Difference in Prices between Treatment and Control

This won’t work because there might be pre-existing differences between treatment and control. How can I be sure I am not capturing those effects?

Idea 3: Combine Ideas 1 and 2

Calculate difference (before and after) for both treatment and control. Whatever happened in the control should have also happened in the treatment. Subtracting the change in the control from the change in the treatment will isolate the treatment effect. This is called difference-in-differences.

Econometrics

Model 1

\[\begin{align}P_{int} &= X_{int}\beta + \delta_1 \text{Floyd}_{t} + \delta_2 \text{Impact}_{i} \\ &+ \delta_3 (\text{Floyd}_{t} \times \text{Impact}_{i}) + \epsilon_{int}\end{align}\]

where:
\(X_{int}\) are variables specific to the property.
Floyd\(_{t}\) is an indicator equal to 1 for periods between trial dates.
Impact\(_i\) is an indicator equal to 1 for properties inside Impact Zones.

\(\delta_3\) is the “treatment effect”: how much more did properties prices change in Impact Zones, relative to nearby properties, when Stop & Frisk was ruled unconstitutional.

If property prices for both areas changed in similar ways, \(\delta_3 \approx 0\).

Time Series

Time Series of Sale Price

Results

Event Study

Heterogeneities

Model 2

\[\begin{align}P_{int} & = X_{int}\beta + \delta_1 \text{Floyd}_{t} + \delta_2 \text{Impact}_{i} \\ & + \delta_3 (\text{Floyd}_{t}\times \text{Impact}_{i}) + \lambda_1 \text{NonWhite}_{it} \\ & + \lambda_2 (\text{Impact}_{i}\times \text{NonWhite}_{it}) + \lambda_3 (\text{Floyd}_{t}\times \text{NonWhite}_{it}) \\ & + \lambda_4 (\text{Floyd}_{t}\times \text{Impact}_{i}\times \text{NonWhite}_{it}) + \epsilon_{int}\end{align}\]

where:
NonWhite is an indicator if the last name of the buyer/seller has a \(<50\)% chance of being white. \(\lambda_4 > 0\) would mean that treated non-white individuals saw a greater price increase than their white counterparts.

Model 2

\[\begin{align} P_{int} &= X_{int}\beta + \delta_1 \text{Floyd}_{t} + \delta_2 \text{Impact}_{i} \\ & + \delta_3 (\text{Floyd}_{t} \times \text{Impact}_{i}) + \gamma_1 (\text{Impact}_{i} \times \text{Exposure}_{it}) \\ & + \gamma_2 (\text{Floyd}_{t} \times \text{Impact}_{i} \times \text{Exposure}_{it}) + \epsilon_{int}\end{align}\]

where:
Exposure is an indicator if the seller owned the property while it was in an active Impact Zone. \(\gamma_2 > 0\) would mean that treated individuals who lived through an Impact Zone saw greater price increases than their counterparts who did not directly experience an Impact Zone.

Results

Results

Results

Conclusion

Conclusion

  1. Contrary to Friedman (2015), the intensive margin of Stop & Frisk itself does not appear to influence housing prices
  2. Values were previously depressed in areas subjected to Operation Impact before Stop & Frisk was ruled to be illegal
  3. The bounce-back in housing prices was not realized by those people who were most heavily subjected to Stop & Frisk beforehand.
    • The literature has documented negative unintended consequences resulting from Stop & Frisk (e.g. education, mental health).
    • The effect Stop & Frisk had on housing prices was about equal to that of an economic recession.

alexcardazzi.github.io

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Bibliography

Bacher-Hicks, Andrew, and Elijah de la Campa. 2021a. “Social costs of proactive policing: The impact of NYC’s Stop and Frisk program on educational attainment.” Working Paper.
———. 2021b. “The impact of New York City’s Stop and Frisk program on crime: The case of police commanders.” Working Paper.
Benı́tez-Silva, Hugo, Selcuk Eren, Frank Heiland, and Sergi Jiménez-Martı́n. 2015. How well do individuals predict the selling prices of their homes? Journal of Housing Economics 29: 12–25.
Caudill, Steven B, Ermanno Affuso, and Ming Yang. 2015. Registered sex offenders and house prices: An hedonic analysis.” Urban Studies 52 (13): 2425–40.
Friedman, Matthew. 2015. “Valuing proactive policing: A hedonic analysis of Stop & Frisk’s amenity value.” Available at SSRN 2695584.
Geller, Amanda, Jeffrey Fagan, Tom Tyler, and Bruce G Link. 2014. Aggressive policing and the mental health of young urban men.” American Journal of Public Health 104 (12): 2321–27.
Ihlanfeldt, Keith, and Tom Mayock. 2010. Panel data estimates of the effects of different types of crime on housing prices.” Regional Science and Urban Economics 40 (2-3): 161–72.
Kim, Seonghoon, and Kwan Ok Lee. 2018. Potential crime risk and housing market responses.” Journal of Urban Economics 108: 1–17.
Legewie, Joscha, and Jeffrey Fagan. 2019. “Aggressive Policing and the Educational Performance of Minority Youth.” American Sociological Review 84 (2): 220–47.
Linden, Leigh, and Jonah E Rockoff. 2008. “Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws.” American Economic Review 98 (3): 1103–27.
MacDonald, John, Jeffrey Fagan, and Amanda Geller. 2016. “The Effects of Local Police Surges on Crime and Arrests in New York City.” PLoS One 11 (6): e0157223.
Pope, Jaren C. 2008. “Fear of Crime and Housing Prices: Household Reactions to Sex Offender Registries.” Journal of Urban Economics 64 (3): 601–14.
Tita, George E, Tricia L Petras, and Robert T Greenbaum. 2006. “Crime and Residential Choice: A Neighborhood Level Analysis of the Impact of Crime on Housing Prices.” Journal of Quantitative Criminology 22 (4): 299.
Tyler, Tom R, Jeffrey Fagan, and Amanda Geller. 2014. “Street Stops and Police Legitimacy: Teachable Moments in Young Urban Men’s Legal Socialization.” Journal of Empirical Legal Studies 11 (4): 751–85.